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WO2017061639A1 - Procédé de comptage de mouvement reposant sur un contexte d'utilisateur, dispositif capteur et dispositif vestimentaire le mettant en œuvre - Google Patents

Procédé de comptage de mouvement reposant sur un contexte d'utilisateur, dispositif capteur et dispositif vestimentaire le mettant en œuvre Download PDF

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Publication number
WO2017061639A1
WO2017061639A1 PCT/KR2015/010532 KR2015010532W WO2017061639A1 WO 2017061639 A1 WO2017061639 A1 WO 2017061639A1 KR 2015010532 W KR2015010532 W KR 2015010532W WO 2017061639 A1 WO2017061639 A1 WO 2017061639A1
Authority
WO
WIPO (PCT)
Prior art keywords
user context
motion counting
motion
sensor data
user
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/KR2015/010532
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English (en)
Korean (ko)
Inventor
보글마이클
권성호
이종성
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
STANDING EGG Inc
Original Assignee
STANDING EGG Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by STANDING EGG Inc filed Critical STANDING EGG Inc
Priority to PCT/KR2015/010532 priority Critical patent/WO2017061639A1/fr
Publication of WO2017061639A1 publication Critical patent/WO2017061639A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P13/00—Indicating or recording presence, absence, or direction, of movement
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor

Definitions

  • the present invention relates to a method of counting a user's motion, and more particularly, to a user context-based motion counting method, a sensor device for performing the same, and a wearable device.
  • a wearable device represents a computer system provided in a form that can be worn on a user's body.
  • the wearable device may measure a user's movement using various sensors and provide a measurement result to the user.
  • the wearable device may manually input the user's exercise motion and count the number of times the received motion motion is repeated.
  • the wearable device may monitor the movement of the user at a specific cycle to provide additional information such as calorie consumption and activity time.
  • An object of the present invention is to provide a user context-based motion counting method that can more accurately count a user's motion.
  • Another technical problem of the present invention is to provide a user context-based motion counting method that can count a user's motion more intuitively and conveniently.
  • Another technical problem of the present invention is to provide a sensor device and a wearable device for performing the user context based motion counting method.
  • a method of counting motion based on a user context extracting at least one feature from sensor data, and generating a user context based on the extracted feature. Identifying, determining a motion counting algorithm based on the identified user context, and counting a user's motion based on the sensor data using the determined motion counting algorithm.
  • the extracted feature may include at least one statistical value of the sensor data.
  • identifying the user context may identify a user context based on the extracted feature using a K-Nearest Neighborhood (KNN) algorithm.
  • KNN K-Nearest Neighborhood
  • determining the motion counting algorithm comprises determining an autocorrelation parameter of the motion counting algorithm based on the identified user context, wherein the first autocorrelation parameter is determined for a first user context.
  • the parameter may be determined, and a second autocorrelation parameter may be determined for the second user context.
  • the autocorrelation parameter may correspond to a repetition period of a peak included in the sensor data.
  • determining the motion counting algorithm comprises: determining a first motion counting algorithm for a first user context based on the identified user context, and a second motion for a second user context The counting algorithm can be determined.
  • the motion counting algorithm includes filtering of sensor data of a predetermined frequency range of the sensor data, and determining the motion counting algorithm is based on the identified user context.
  • the filtering setting may be determined, but the first filtering setting may be determined for the first user context, and the second filtering setting may be determined for the second user context.
  • the method may further comprise acquiring the sensor data from an acceleration sensor or a gyro sensor.
  • the sensor device may perform any one of the above-described user context-based motion counting method.
  • the wearable device for solving the above technical problem may perform any one of the above-described user context based motion counting method.
  • the motion of the user can be counted more accurately.
  • FIG. 1 is a flowchart schematically illustrating a user context based motion counting method according to an embodiment of the present invention.
  • FIG. 2 is a diagram schematically illustrating identifying a user context using the KNN algorithm.
  • FIG. 3 is a diagram schematically illustrating determining autocorrelation parameters based on a user context.
  • FIG. 4 is a diagram schematically illustrating determining filtering settings for sensor data based on a user context.
  • FIG. 5 is a block diagram schematically illustrating a wearable device for performing a user context based motion counting method according to an embodiment of the present invention.
  • FIG. 6 is a diagram schematically illustrating a state in which the wearable device of FIG. 5 is worn on a user's body.
  • FIG. 7 is a block diagram schematically illustrating a wearable device for performing a user context based motion counting method according to another embodiment of the present invention.
  • FIG. 8 is a block diagram schematically illustrating a sensor hub for performing a user context based motion counting method according to an embodiment of the present invention.
  • FIG. 9 is a block diagram schematically illustrating a sensor hub for performing a user context based motion counting method according to another embodiment of the present invention.
  • first, second, etc. are used to describe various elements, components and / or sections, these elements, components and / or sections are of course not limited by these terms. These terms are only used to distinguish one element, component or section from another element, component or section. Therefore, the first device, the first component, or the first section mentioned below may be a second device, a second component, or a second section within the technical spirit of the present invention.
  • FIG. 1 is a flowchart schematically illustrating a user context based motion counting method according to an embodiment of the present invention.
  • sensor data is obtained from an acceleration sensor, a gyro sensor, or at least one sensor coupled thereto.
  • Sensor data can be acceleration data in three axis directions (e.g., X axis, Y axis, Z axis) or three axis directions (e.g., Pitch axis, Yaw axis, Roll axis)
  • the angular velocity data may include, but is not limited thereto.
  • a process such as predetermined sampling or quantization of sensor data may be provided.
  • At step S20 at least one feature is extracted from the sensor data.
  • a characteristic represents an aspect or component in which sensor data is distinguished from other sensor data.
  • the feature may represent an entire portion of the sensor data or may represent a portion of the sensor data.
  • the feature may include at least one statistical value of sensor data.
  • the statistical value may include a maximum value, a minimum value, a median value, an average value, an interquartile range (IQR) value, a root mean square (RMS) value, and the like of a predetermined number of (sampled) sensor data.
  • IQR interquartile range
  • RMS root mean square
  • the feature may be extracted by any processing on the sensor data.
  • the user context is identified based on the extracted feature.
  • the user context represents a situation of the user or information defining the same.
  • the user context may be associated with the type of action that may specify the movement of the user. For example, the user context may be classified into various movement operations such as push up, biceps curls, and rowing, but is not limited thereto.
  • the user context may be identified using the KNN algorithm.
  • 2 is a diagram schematically illustrating identifying a user context using the KNN algorithm. 2
  • a plurality of features are processed using the KNN algorithm.
  • the KNN algorithm a plurality of features may be compared to previously classified or learned features and classified into groups having the same or similar trends. By combining the classification results, a specific user context can be derived.
  • Settings regarding the k value and the distance between neighboring features may be variously adjusted according to embodiments. Although not explicitly illustrated, weights of high importance, distance, and the like for some features may be applied.
  • a detailed description of the KNN algorithm will be omitted since it may obscure the subject matter of the present invention.
  • any machine learning algorithm that is not illustrated may be used for identification of the user context.
  • a motion counting algorithm is determined based on the identified user context.
  • autocorrelation parameters of the motion counting algorithm may be determined together based on the identified user context.
  • the autocorrelation parameter may be determined differently according to the user context. That is, the first autocorrelation parameter may be determined for the first user context, and the second autocorrelation parameter may be determined for the second user context.
  • the autocorrelation parameter may be used to remove noise or counting errors within the motion counting algorithm.
  • the autocorrelation parameter may correspond to the repetition period of the peaks included in the sensor data.
  • the peak may be a factor or factor for counting the user's movement as described below.
  • 3 is a diagram schematically illustrating determining autocorrelation parameters based on a user context. Referring to FIG. 3, a plurality of peaks p0 to p2 may be included in the sensor data of a predetermined time range. Peaks can be selected based on their amplitude and the time interval between previous peaks. On the other hand, the repeating pattern of the peak may vary depending on the user context (user's motion).
  • the repetition period of the peak that is, the time interval between the previous peak and the current peak is 100 msec
  • the current peak is presumed to be due to noise or error and is not used for counting.
  • the reference time interval for the repetition period of the peak is tr1
  • p1 may be selected as the peak for counting
  • tr2 if the reference time interval is tr2, p1 next to p0 may be selected. Ignored and p2 may be selected as the peak for counting.
  • At least some motion counting algorithms may include filtering for sensor data in a predetermined frequency range of the sensor data to remove noise or counting errors. And, when the motion counting algorithm is determined, filtering settings may be determined together based on the identified user context. Filtering settings may be determined differently according to the user context. That is, the first filtering setting may be determined for the first user context, and the second filtering setting may be determined for the second user context.
  • FIG. 4 is a diagram schematically illustrating determining filtering settings for sensor data based on a user context.
  • a plurality of filtering settings that define a predetermined pass band is shown.
  • the pass bands of fl1 and fh1 are set to cutoff frequencies according to the first filtering setting, and when fl2 and fh2 are set to cutoff frequencies according to the second filtering setting.
  • the band pass filter is illustrated as an example, but is not limited thereto, and the filtering setting may be provided in a substantially same manner for the high pass filter and the low pass filter.
  • a separate motion counting algorithm can be determined based on the identified user context (in the case of a given motion moving in a completely different way than other actions).
  • a first motion counting algorithm may be determined for the first user context
  • a second motion counting algorithm may be determined for the second user context.
  • step S50 the movement of the user is counted based on the sensor data using the determined motion counting algorithm.
  • the sensor data of the axis having the largest change among the sensor data of the three axes may be selected, and the peak in the sensor data of the selected axis may be counted.
  • the method of counting movements may be variously modified according to specific embodiments.
  • FIG. 5 is a block diagram schematically illustrating a wearable device for performing a user context based motion counting method according to an embodiment of the present invention.
  • the wearable device 100 may include an acceleration sensor 110, a gyro sensor 120, a storage unit 130, an input unit 140, an output unit 150, a controller 160, and a power supply unit 170. ).
  • the acceleration sensor 110 detects acceleration in three axis directions (for example, X axis, Y axis, and Z axis). Although only one acceleration sensor is illustrated in FIG. 5, the present invention is not limited thereto, and a plurality of acceleration sensors capable of detecting acceleration in one axial direction may be provided.
  • the acceleration sensor 110 may transmit acceleration data in three axes to the controller 160.
  • the gyro sensor 120 detects angular velocities in three axis directions (for example, a pitch axis, a yaw axis, and a roll axis). Although only one gyro sensor is illustrated in FIG. 5, the present invention is not limited thereto, and a plurality of gyro sensors capable of detecting an angular velocity in one axial direction may be provided.
  • the gyro sensor 120 may transmit angular velocity data in three axes to the controller 160.
  • the storage unit 130 stores various data and commands.
  • the storage unit 130 may store various software modules including system software for operating the wearable device 100 and applications in which a user context-based motion counting method according to an embodiment of the present invention is implemented.
  • the storage unit 130 may be a random access memory (RAM), read only memory (ROM), erasable-programmable ROM (EPROM), electrically EPROM (EEPROM), flash memory, a removable disk, or well known in the art. Any type of computer readable recording medium may be included.
  • the input unit 140 receives various information from the user.
  • the input unit 140 may include various input means such as keys, buttons, switches, wheels, and touch pads.
  • the output unit 150 notifies the user of various kinds of information.
  • the output unit 150 may output information in the form of text, video or audio.
  • the output unit 150 may include a display module 151 and a speaker module 152.
  • the display module 151 may be a liquid crystal display (LCD), a thin film transistor (TFT) LCD, an organic light emitting diode (OLED), a flexible display, a three-dimensional display, an electronic ink display, or a technique well known in the art. It may be provided in any form.
  • the controller 160 controls other components to control the overall operation of the wearable device 100.
  • the controller 160 may perform various software modules including system software for operating the wearable device 100 and applications in which a user context-based motion counting method according to an embodiment of the present invention is implemented.
  • the controller 160 may filter the sensor data transmitted from the sensors 110 and 120, including the high pass filter 161, the low pass filter 162, and the band pass filter 163.
  • the controller 160 may filter the sensor data by selecting at least one filter among the plurality of filters 161 ⁇ 163. Meanwhile, the high pass filter 161, the low pass filter 162, and the band pass filter 163 may be provided as independent components to the outside of the controller 160 or may be internal components of the respective sensors 110 and 120. May be provided.
  • the power supply unit 170 supplies power required for the operation of the acceleration sensor 110, the gyro sensor 120, the storage 130, the input unit 140, the output unit 150, and the controller 160.
  • the power supply unit 170 may include a built-in battery or convert power supplied from the outside into a power suitable for the above components.
  • the wearable device 100 may be modified to include more components or fewer components.
  • FIG. 6 is a diagram schematically illustrating a state in which the wearable device of FIG. 5 is worn on a user's body.
  • the wearable device 100 described with reference to FIG. 5 may be worn on a user's body.
  • the wearable device 100 may be worn on an extremity such as an arm or a leg of the user, or may be worn on the user's torso, but is not limited thereto, and may include other parts (head, hands, feet, etc.) It may be worn on at least part of the user's body.
  • the wearable device 100 may be provided inside any item (hat, gloves, shoes, etc.) worn on the user's body.
  • FIG. 7 is a block diagram schematically illustrating a wearable device for performing a user context based motion counting method according to another embodiment of the present invention.
  • a wearable device for performing a user context based motion counting method according to another embodiment of the present invention.
  • duplicated descriptions of components that are the same as the wearable device 100 described with reference to FIG. 5 will be omitted.
  • the wearable device 200 further includes a wireless communication unit 230 and a vibrator 260 as compared with the wearable device 100 described with reference to FIG. 5.
  • the wireless communication unit 230 may wirelessly communicate with an external device (such as a server or a user terminal).
  • the wireless communication unit 230 may wirelessly communicate with an external device by using a wireless communication method such as mobile communication, WiBro, Wi-Fi, Bluetooth, Zigbee, ultrasonic wave, infrared ray, or RF (Radio Frequency). have.
  • the wireless communication unit 230 may transmit data and / or information received from the external device to the controller 280, and may transmit data and / or information transmitted from the controller 280 to the external device.
  • the wireless communication unit 230 may include a mobile communication module, a short-range communication module and the like.
  • the vibrator 260 may perform a vibration notification for notifying the user of various kinds of information.
  • the wearable device 200 may transmit / receive various types of information such as sensor data, a user context, a motion counting algorithm, and a motion counting result with various servers or user devices (not shown).
  • the wearable device according to the embodiment of the present invention may be provided as any computer system wearable on the body of the user, which is not illustrated.
  • FIG. 8 is a block diagram schematically illustrating a sensor hub for performing a user context based motion counting method according to an embodiment of the present invention.
  • the sensor hub 1000 may include a processing device 1100, a MEMS device 1200, and an application specific integrated circuit (ASIC) device 1300.
  • the MEMS device 1200 may be an acceleration sensor or a gyro sensor, but is not limited thereto.
  • the ASIC device 1300 may process the sensing signal of the MEMS device 1200.
  • the processing device 1100 may function as a coprocessor for professionally performing sensor data processing on behalf of the application processor.
  • the processing device 1100 may execute various software modules including an application in which a user context-based motion counting method according to an embodiment of the present invention is implemented.
  • FIG. 9 is a block diagram schematically illustrating a sensor hub for performing a user context based motion counting method according to another embodiment of the present invention.
  • the sensor hub 2000 may include a plurality of MEMS devices 2200 and 2400 and a plurality of ASIC devices 2300 and 2500.
  • the first MEMS device 2200 may be an acceleration sensor
  • the second MEMS device 2400 may be a gyro sensor, but is not limited thereto.
  • the plurality of ASIC devices 2300 and 2500 may process sensing signals of the corresponding MEMS devices 2200 and 2400, respectively.
  • the processing device 2100 may function as a coprocessor for professionally performing sensor data processing on behalf of the application processor.
  • the processing device 2100 may execute various software modules including an application in which the user context-based motion counting method according to an embodiment of the present invention is implemented. Unlike shown, three or more MEMS devices and ASIC devices may be provided within the sensor hub 2000.
  • the method described in connection with an embodiment of the present invention may be implemented as a software module performed by a processor.
  • the software module may reside in RAM, ROM, EPROM, EEPROM, flash memory, hard disk, removable disk, CD-ROM, or any form of computer readable recording medium well known in the art. .

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
  • User Interface Of Digital Computer (AREA)

Abstract

L'invention concerne un procédé de comptage de mouvement reposant sur un contexte d'utilisateur, un dispositif capteur et un dispositif vestimentaire le mettant en œuvre. Le procédé de comptage de mouvement reposant sur un contexte d'utilisateur comprend les étapes consistant à : extraire au moins une caractéristique de données de capteur ; identifier un contexte d'utilisateur sur la base de la caractéristique extraite ; déterminer un algorithme de comptage de mouvement sur la base du contexte d'utilisateur identifié ; et compter un mouvement de l'utilisateur sur la base des données de capteur à l'aide de l'algorithme de comptage de mouvement déterminé.
PCT/KR2015/010532 2015-10-06 2015-10-06 Procédé de comptage de mouvement reposant sur un contexte d'utilisateur, dispositif capteur et dispositif vestimentaire le mettant en œuvre Ceased WO2017061639A1 (fr)

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PCT/KR2015/010532 WO2017061639A1 (fr) 2015-10-06 2015-10-06 Procédé de comptage de mouvement reposant sur un contexte d'utilisateur, dispositif capteur et dispositif vestimentaire le mettant en œuvre

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PCT/KR2015/010532 WO2017061639A1 (fr) 2015-10-06 2015-10-06 Procédé de comptage de mouvement reposant sur un contexte d'utilisateur, dispositif capteur et dispositif vestimentaire le mettant en œuvre

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100647905B1 (ko) * 2005-06-03 2006-11-23 한국전자통신연구원 수면 환경 제어 시스템 및 그 방법
KR20070047096A (ko) * 2005-11-01 2007-05-04 한국전자통신연구원 가속도 신호를 이용한 동작 입력 장치, 호스트 정합 장치 및 동작 인식 방법
KR20110100986A (ko) * 2010-03-05 2011-09-15 에스케이텔레콤 주식회사 휴대 단말기의 잠금 해제 방법 및 그 휴대 단말기
KR20130101505A (ko) * 2010-08-06 2013-09-13 구글 인코포레이티드 컨텍스트에 기초한 입력 명확화
KR20130124143A (ko) * 2012-11-30 2013-11-13 삼성전자주식회사 공간상의 상호 작용을 이용한 단말의 제어 방법 및 그 단말

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100647905B1 (ko) * 2005-06-03 2006-11-23 한국전자통신연구원 수면 환경 제어 시스템 및 그 방법
KR20070047096A (ko) * 2005-11-01 2007-05-04 한국전자통신연구원 가속도 신호를 이용한 동작 입력 장치, 호스트 정합 장치 및 동작 인식 방법
KR20110100986A (ko) * 2010-03-05 2011-09-15 에스케이텔레콤 주식회사 휴대 단말기의 잠금 해제 방법 및 그 휴대 단말기
KR20130101505A (ko) * 2010-08-06 2013-09-13 구글 인코포레이티드 컨텍스트에 기초한 입력 명확화
KR20130124143A (ko) * 2012-11-30 2013-11-13 삼성전자주식회사 공간상의 상호 작용을 이용한 단말의 제어 방법 및 그 단말

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